Energy Metabolism-related Genes Research Articles

A Dose-Response Study on Functional and Transcriptomic Effects of FSH on Ex Vivo Mouse Folliculogenesis.

Follicle-stimulating hormone (FSH) binds to its membrane receptor (FSHR) in granulosa cells to activate various signal transduction pathways and drive the gonadotropin-dependent phase of folliculogenesis. Both FSH insufficiency (due to genetic or nongenetic factors) and FSH excess (as encountered with ovarian stimulation in assisted reproductive technology [ART]) can cause poor female reproductive outcomes, but the underlying molecular mechanisms remain elusive. Herein, we conducted single-follicle and single-oocyte RNA sequencing analysis along with other approaches in an ex vivo mouse folliculogenesis and oogenesis system to investigate the effects of different concentrations of FSH on key follicular events. Our study revealed that a minimum FSH threshold is required for follicle maturation into the high estradiol-secreting preovulatory stage, and such threshold is moderately variable among individual follicles between 5 and 10 mIU/mL. FSH at 5, 10, 20, and 30 mIU/mL induced distinct expression patterns of follicle maturation-related genes, follicular transcriptomics, and follicular cAMP levels. RNA sequencing analysis identified FSH-stimulated activation of G proteins and downstream canonical and novel signaling pathways that may critically regulate follicle maturation, including the cAMP/PKA/CREB, PI3K/AKT/FOXO1, and glycolysis pathways. High FSH at 20 and 30 mIU/mL resulted in noncanonical FSH responses, including premature luteinization, high production of androgen and proinflammatory factors, and reduced expression of energy metabolism-related genes in oocytes. Together, this study improves our understanding of gonadotropin-dependent folliculogenesis and provides crucial insights into how high doses of FSH used in ART may impact follicular health, oocyte quality, pregnancy outcome, and systemic health.

Construction and evaluation of a metabolic correlation diagnostic model for diabetes based on machine learning algorithms.

Diabetes mellitus (DM) is a prevalent chronic disease marked by significant metabolic dysfunctions. Understanding its molecular mechanisms is vital for early diagnosis and treatment strategies. We used datasets GSE7014, GSE25724, and GSE156248 from the GEO database to build a diagnostic model for DM using Random Forest (RF) and LASSO regression models. GSE20966 served as a validation cohort. DM patients were classified into two subtypes for functional enrichment analysis. Expression levels of key diagnostic genes were validated using quantitative real-time PCR (qRT-PCR) on Peripheral Blood Mononuclear Cells (PBMCs) from DM patients and healthy controls, focusing on CXCL12 and PPP1R12B with GAPDH as the internal control. After de-batching the datasets, we identified 131 differentially expressed genes (DEGs) between DM and control groups, with 70 up-regulated and 61 down-regulated. Enrichment analysis revealed significant down-regulation in the IL-12 signaling pathway, JAK signaling post-IL-12 stimulation, and the ferroptosis pathway in DM. Five genes (CXCL12, MXRA5, UCHL1, PPP1R12B, and C7) were identified as having diagnostic value. The diagnostic model showed high accuracy in both the training and validation cohorts. The gene set also enabled the subclassification of DM patients into groups with distinct functional traits. qRT-PCR results confirmed the bioinformatics findings, particularly the up-regulation of CXCL12 and PPP1R12B in DM patients. Our study pinpointed seven energy metabolism-related genes differentially expressed in DM and controls, with five holding diagnostic value. Our model accurately diagnosed DM and facilitated patient subclassification, offering new insights into DM pathogenesis.

Cd pollution in agroforestry ecosystems: An abiotic factor affecting the passive and active lethal efficiency of Beauveria bassiana to Lymantria dispar larvae

This study evaluated the effects of cadmium (Cd) exposure on the passive and active lethal efficiency of Beauveria bassiana (Bb) to Lymantria dispar larvae and analyzed the corresponding mechanism from mycelial vegetative growth, fungal and host nutrient competition, and fungal spore performance. The results showed that the passive lethal efficiency of Bb to Cd-exposed L. dispar larvae was significantly higher than that of larvae not exposed to Cd. After Bb infection, the fungal biomass in living larvae and the mycelium encapsulation index of dead larvae were significantly decreased under Cd exposure. Cd exposure damaged the mycelial structure, as well as inhibited the mycelial growth and sporulation quantity. A total of 15 and 39 differentially accumulated mycotoxin metabolites were identified in Bb mycelia treated with low Cd and high Cd, respectively, and the contents of these differentially accumulated mycotoxins in the low Cd and high Cd treatment groups were overall lower than those in the control group. Nutrient content and energy metabolism-related gene expression were significantly decreased in Cd-exposed larvae, both before and after Bb infection. Trehalose supplementation alleviated the nutritional deficiency of larvae under the combined treatment of Cd and Bb and decreased the larval susceptibility to Bb. Compared with untreated Bb, the lethal efficiency of low Cd-exposed Bb to larvae increased significantly, while high Cd-exposed Bb was significantly less lethal to larvae. Cd exposure promoted at low concentrations but inhibited the hydrophobicity and adhesion of spores at higher concentrations. Spore germination rate and stress resistance of Bb decreased significantly under the treatment of both Cd concentrations. Taken together, heavy metals can be regarded as an abiotic environmental factor that directly affects the lethal efficiency of Bb to insect pests.

Development and experimental validation of an energy metabolism-related gene signature for diagnosing of osteoporosis

Osteoporosis is usually caused by excessive bone resorption and energy metabolism plays a critical role in the development of osteoporosis. However, little is known about the role of energy metabolism-related genes in osteoporosis. This study aimed to explore the important energy metabolism-related genes involved in the development of osteoporosis and develop a diagnosis signature for osteoporosis. The GSE56814, GSE62402, and GSE7158 datasets were downloaded from the NCBI Gene Expression Omnibus. The intersection of differentially expressed genes between high and low levels of body mineral density (BMD) and genes related to energy metabolism were screened as differentially expressed energy metabolism genes (DE-EMGs). Subsequently, a DE-EMG-based diagnostic model was constructed and differential expression of genes in the model was validated by RT-qPCR. Furthermore, a receiver operating characteristic curve and nomogram model were constructed to evaluate the predictive ability of the diagnostic model. Finally, the immune cell types in the merged samples and networks associated with the selected optimal DE-EMGs were constructed. A total of 72 overlapped genes were selected as DE-EMGs, and a five DE-EMG based diagnostic model consisting B4GALT4, ADH4, ACAD11, B4GALT2, and PPP1R3C was established. The areas under the curve of the five genes in the merged training dataset and B4GALT2 in the validation dataset were 0.784 and 0.790, respectively. Moreover, good prognostic prediction ability was observed using the nomogram model (C index = 0.9201; P = 5.507e−14). Significant differences were observed in five immune cell types between the high- and low-BMD groups. These included central memory, effector memory, and activated CD8 T cells, as well as regulatory T cells and activated B cells. A network related to DE-EMGs was constructed, including hsa-miR-23b-3p, DANCR, 17 small-molecule drugs, and two Kyoto Encyclopedia of Genes and Genomes pathways, including metabolic pathways and pyruvate metabolism. Our findings highlighted the important roles of DE-EMGs in the development of osteoporosis. Furthermore, the DANCR/hsa-miR-23b-3p/B4GALT4 axis might provide novel molecular insights into the process of osteoporosis development.

Association of leptin receptor polymorphisms with susceptibility of non-small cell lung cancer: Evidence from 2249 subjects.

Non-small cell lung cancer (NSCLC) is increasing dramatically. It is believed that energy metabolism-related genes could play an important role in etiology of NSCLC. In this study, we sought to assess the correlation between three LEPR single nucleotide polymorphisms (rs1137101, rs1137100 and rs6588147) with NSCLS susceptibility. In total, 1193 NSCLC cases and 1056 controls were included. SNPscan™ genotyping method was used to analyze the genotypes of LEPR polymorphisms. Compared to rs6588147 GG in LEPR gene, this study identified a protective role of LEPR rs6588147 GA and GA/AA for the occurrence of NSCLC (GA vs. GG [p = 0.021] and GA/AA vs. GG [p = 0.030]). As well, we found that a protective role of LEPR rs6588147 for the occurrence of non-SCC subgroup (p < 0.05). By logistic regression analysis, we found that the rs6588147 A allele related genotypes might play a protective role for the occurrence of NSCLC in drinking, BMI ≥24 kg/m2, smoking and male subgroups. We also found that the rs1137101 A allele related genotypes played a protective role for the occurrence of NSCLC in male, younger participants (under 59 years) and overweight/obesity (BMI ≥24 kg/m2) subgroups. We found that LEPR Ars1037100Ars1037101Ars6588147 haplotype might play a protective role for the occurrence of NSCLC (p = 0.013). In addition, our findings indicated that LEPR rs1137100 G>A SNP might increase the risk of lymph node metastases (p = 0.038). This study highlights that LEPR rs6588147, rs1137101 genotypes and LEPR Ars1037100Ars1037101Ars6588147 haplotype are correlated with the occurrence of NSCLC. LEPR rs1137100 G>A SNP increases the risk of lymph node metastases.

Identification and validation of DHCR7 as a diagnostic biomarker involved in the proliferation and mitochondrial function of breast cancer.

Energy metabolism has a complex intersection with pathogenesis and development of breast cancer (BC). This allows for the possibility of identifying energy-metabolism-related genes (EMRGs) as novel prognostic biomarkers for BC. 7-dehydrocholesterol reductase (DHCR7) is a key enzyme of cholesterol biosynthesis involved in many cancers, and in this paper, we investigate the effects of DHCR7 on the proliferation and mitochondrial function of BC. EMRGs were identified from the Gene Expression Omnibus (GEO) and MSigDB databases using bioinformatics methods. Key EMRGs of BC were then identified and validated by functional enrichment analysis, interaction analysis, weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) regression, Cox analysis, and immune infiltration. Western blot, qRT-PCR, immunohistochemistry (IHC), MTT assay, colony formation assay and flow cytometry assay were then used to analyze DHCR7 expression and its biological effects on BC cells. We identified 31 EMRGs in BC. These 31 EMRGs and related transcription factors (TFs), miRNAs, and drugs were enriched in glycerophospholipid metabolism, glycoprotein metabolic process, breast cancer, and cell cycle. Crucially, DHCR7 was a key EMRG in BC identified and validated by WGCNA, LASSO regression and receiver operating characteristic (ROC) curve analysis. High DHCR7 expression was significantly associated with tumor immune infiltration level, pathological M, and poor prognosis in BC. In addition, DHCR7 knockdown inhibited cell proliferation, induced apoptosis and affected mitochondrial function in BC cells. DHCR7 was found to be a key EMRG up-regulated in BC cells. This study is the first to our knowledge to report that DHCR7 acts as an oncogene in BC, which might become a novel therapeutic target for BC patients.

Effects of exogenous glycine betaine on growth and development of tomato seedlings under cold stress.

Low temperature is a type of abiotic stress affecting the tomato (Solanum lycopersicum) growth. Understanding the mechanisms and utilization of exogenous substances underlying plant tolerance to cold stress would lay the foundation for improving temperature resilience in this important crop. Our study is aiming to investigate the effect of exogenous glycine betaine (GB) on tomato seedlings to increase tolerance to low temperatures. By treating tomato seedlings with exogenous GB under low temperature stress, we found that 30 mmol/L exogenous GB can significantly improve the cold tolerance of tomato seedlings. Exogenous GB can influence the enzyme activity of antioxidant defense system and ROS levels in tomato leaves. The seedlings with GB treatment presented higher Fv/Fm value and photochemical activity under cold stress compared with the control. Moreover, analysis of high-throughput plant phenotyping of tomato seedlings also supported that exogenous GB can protect the photosynthetic system of tomato seedlings under cold stress. In addition, we proved that exogenous GB significantly increased the content of endogenous abscisic acid (ABA) and decreased endogenous gibberellin (GA) levels, which protected tomatoes from low temperatures. Meanwhile, transcriptional analysis showed that GB regulated the expression of genes involved in antioxidant capacity, calcium signaling, photosynthesis activity, energy metabolism-related and low temperature pathway-related genes in tomato plants. In conclusion, our findings indicated that exogenous GB, as a cryoprotectant, can enhance plant tolerance to low temperature by improving the antioxidant system, photosynthetic system, hormone signaling, and cold response pathway and so on.

Mitochondrial energy metabolism-related gene signature as a prognostic indicator for pancreatic adenocarcinoma.

Background: Pancreatic adenocarcinoma (PAAD) is a highly malignant gastrointestinal tumor and is associated with an unfavorable prognosis worldwide. Considering the effect of mitochondrial metabolism on the prognosis of pancreatic cancer has rarely been investigated, we aimed to establish prognostic gene markers associated with mitochondrial energy metabolism for the prediction of survival probability in patients with PAAD. Methods: Gene expression data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases, and the mitochondrial energy metabolism-related genes were obtained from the GeneCards database. Based on mitochondrial energy metabolism score (MMs), differentially expressed MMRGs were established for MMs-high and MMs-low groups using ssGSEA. After the univariate Cox and least absolute and selection operator (LASSO) analyses, a prognostic MMRG signature was used in the multivariate Cox proportional regression model. Survival and immune cell infiltration analyses were performed. In addition, a nomogram based on the risk model was used to predict the survival probability of patients with PAAD. Finally, the expression of key genes was verified using quantitative polymerase chain reaction and immunohistochemical staining. Intro cell experiments were performed to evaluated the proliferation and invasion of pancreatic cancer cells. Results: A prognostic signature was constructed consisting of two mitochondrial energy metabolism-related genes (MMP11, COL10A1). Calibration and receiver operating characteristic (ROC) curves verified the good predictability performance of the risk model for the survival rate of patients with PAAD. Finally, immune-related analysis explained the differences in immune status between the two subgroups based on the risk model. The high-risk score group showed higher estimate, immune, and stromal scores, expression of eight checkpoint genes, and infiltration of M0 macrophages, which might indicate a beneficial response to immunotherapy. The qPCR results confirmed high expression of MMP11 in pancreatic cancer cell lines, and IHC also verified high expression of MMP11 in clinical pancreatic ductal adenocarcinoma tissues. In vitro cell experiments also demonstrated the role of MMP11 in cell proliferation and invasion. Conclusion: Our study provides a novel two-prognostic gene signature-based on MMRGs-that accurately predicted the survival of patients with PAAD and could be used for mitochondrial energy metabolism-related therapies in the future.

Fibroblast activation protein promotes progression of hepatocellular carcinoma via regulating the immunity.

Fibroblast activation protein (FAP) has been indicated to express in cancer-associated fibroblasts (CAFs) in most cancers. This work was dedicated to exploring FAP's effects on hepatocellular carcinoma(HCC). The data were extracted from The Cancer Genome Atlas, Gene Expression Omnibus, ImmPort, and Reactome databases. The correlation between FAP and HCC patients' prognosis was explored via survival analysis. The qRT-PCR and western blot analysis were used to analyze the FAP mRNA and protein expression levels, respectively. The cell proliferation and apoptosis were determined using the cell counting kit-8 assay kit and Annexin V-FITC/PI apoptosis kit, respectively. The HCC patients with FAP overexpression displayed a worse prognosis. The FAP expression was positively associated with the infiltration levels of tumor purity, B cell, CD8 + T cell, CD4 + T cell, macrophage, neutrophil, and dendritic cell. The optimal nine immune related genes were screened between two groups (FAP high vs. low). Moreover, we identified 24 energy metabolism related genes (FAP high vs. low) and these 24 genes were highly expressed in the high FAP expression group. The FAP expression had a significant positive correlation with the expression of PD-1, CTLA4, PDL-1, and PDL-2. The FAP overexpression promoted proliferation and migration while inhibiting the apoptosis of HCC cells. The FAP overexpression promoted the progression of HCC by regulating the immunity to affect the prognosis of HCC patients, thereby serving as a poor prognostic marker for HCC patients.

Single-cell RNA combined with bulk RNA analysis to explore oxidative stress and energy metabolism factors and found a new prostate cancer oncogene MXRA8.

Prostate cancer is the most common malignancy among men worldwide, and its diagnosis and treatment are challenging due to its heterogeneity. Integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data, we identified two molecular subtypes of prostate cancer based on dysregulated genes involved in oxidative stress and energy metabolism. We constructed a risk score model (OMR) using common differentially expressed genes, which effectively evaluated prostate cancer prognosis. Our analysis demonstrated a significant correlation between the risk score model and various factors, including tumor immune microenvironment, genomic variations, chemotherapy resistance, and immune response. Notably, patients with low-risk scores exhibited increased sensitivity to chemotherapy and immunotherapy compared to those with high-risk scores, indicating the model's potential to predict patient response to treatment. Additionally, our investigation of MXRA8 in prostate cancer showed significant upregulation of this gene in the disease as confirmed by PCR and immunohistochemistry. Functional assays including CCK-8, transwell, plate cloning, and ROS generation assay demonstrated that depletion of MXRA8 reduced the proliferative, invasive, migratory capabilities of PC-3 cells, as well as their ROS generation capacity. Our study highlights the potential of oxidative stress and energy metabolism-related genes as prognostic markers and therapeutic targets in prostate cancer. The integration of scRNA-seq and bulk RNA-seq data enables a better understanding of prostate cancer heterogeneity and promotes personalized treatment development. Additionally, we identified a novel oncogene MXRA8 in prostate cancer.

Diagnostic potential of energy metabolism-related genes in heart failure with preserved ejection fraction.

Heart failure with preserved ejection fraction (HFpEF) is associated with changes in cardiac metabolism that affect energy supply in the heart. However, there is limited research on energy metabolism-related genes (EMRGs) in HFpEF. The HFpEF mouse dataset (GSE180065, containing heart tissues from 10 HFpEF and five control samples) was sourced from the Gene Expression Omnibus database. Gene expression profiles in HFpEF and control groups were compared to identify differentially expressed EMRGs (DE-EMRGs), and the diagnostic biomarkers with diagnostic value were screened using machine learning algorithms. Meanwhile, we constructed a biomarker-based nomogram model for its predictive power, and functionality of diagnostic biomarkers were conducted using single-gene gene set enrichment analysis, drug prediction, and regulatory network analysis. Additionally, consensus clustering analysis based on the expression of diagnostic biomarkers was utilized to identify differential HFpEF-related genes (HFpEF-RGs). Immune microenvironment analysis in HFpEF and subtypes were performed for analyzing correlations between immune cells and diagnostic biomarkers as well as HFpEF-RGs. Finally, qRT-PCR analysis on the HFpEF mouse model was used to validate the expression levels of diagnostic biomarkers. We selected 5 biomarkers (Chrna2, Gnb3, Gng7, Ddit4l, and Prss55) that showed excellent diagnostic performance. The nomogram model we constructed demonstrated high predictive power. Single-gene gene set enrichment analysis revealed enrichment in aerobic respiration and energy derivation. Further, various miRNAs and TFs were predicted by Gng7, such as Gng7-mmu-miR-6921-5p, ETS1-Gng7. A lot of potential therapeutic targets were predicted as well. Consensus clustering identified two distinct subtypes of HFpEF. Functional enrichment analysis highlighted the involvement of DEGs-cluster in protein amino acid modification and so on. Additionally, we identified five HFpEF-RGs (Kcnt1, Acot1, Kcnc4, Scn3a, and Gpam). Immune analysis revealed correlations between Macrophage M2, T cell CD4+ Th1 and diagnostic biomarkers, as well as an association between Macrophage and HFpEF-RGs. We further validated the expression trends of the selected biomarkers through experimental validation. Our study identified 5 diagnostic biomarkers and provided insights into the prediction and treatment of HFpEF through drug predictions and network analysis. These findings contribute to a better understanding of HFpEF and may guide future research and therapy development.

Sustained PGC-1α2 or PGC-1α3 expression induces astrocyte dysfunction and degeneration

Peroxisome proliferator-activated receptor-gamma coactivator-1α (PGC-1α) transcriptional coactivators are key regulators of energy metabolism-related genes and are expressed in energy-demanding tissues. There are several PGC-1α variants with different biological functions in different tissues. The brain is one of the tissues where the role of PGC-1α isoforms remains less explored. Here, we used a toxin-based mouse model of Parkinson's disease (PD) and observed that the expression levels of variants PGC-1α2 and PGC-1α3 in the nigrostriatal pathway increases at the onset of dopaminergic cell degeneration. This increase occurs concomitant with an increase in glial fibrillary acidic protein levels. Since PGC-1α coactivators regulate cellular adaptive responses, we hypothesized that they could be involved in the modulation of astrogliosis induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Therefore, we analysed the transcriptome of astrocytes transduced with expression vectors encoding PGC-1α1 to 1α4 by massively parallel sequencing (RNA-seq) and identified the main cellular pathways controlled by these isoforms. Interestingly, in reactive astrocytes the inflammatory and antioxidant responses, adhesion, migration, and viability were altered by PGC-1α2 and PGC-1α3, showing that sustained expression of these isoforms induces astrocyte dysfunction and degeneration. This work highlights PGC-1α isoforms as modulators of astrocyte reactivity and as potential therapeutic targets for the treatment of PD and other neurodegenerative disorders.

The mitochondrial energy metabolism pathway-related signature predicts prognosis and indicates immune microenvironment infiltration in osteosarcoma.

Abnormalities in the mitochondrial energy metabolism pathways are closely related to the occurrence and development of many cancers. Furthermore, abnormal genes in mitochondrial energy metabolism pathways may be novel targets and biomarkers for the diagnosis and treatment of osteosarcoma. In this study, we aimed to establish a mitochondrial energy metabolism-related gene signature for osteosarcoma prognosis. We first obtained differentially expressed genes based on the metastatic status of 84 patients with osteosarcoma from the TARGET database. After Venn analysis of differentially expressed genes and mitochondrial energy metabolism pathway-related genes (MMRGs), 2 key genes were obtained using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analysis. Next, we used these 2 genes to establish a prognostic signature. Subsequent analyses elucidated the correlation between these 2 key genes with clinical features and 28 types of immune cells. Pathway changes in osteosarcoma pathogenesis under different metastatic states were clarified using gene set enrichment analysis (GSEA) of differentially expressed genes. A gene signature composed of 2 key prognosis-related genes (KCNJ5 and PFKFB2) was identified. A risk score was calculated based on the gene signature, which divided osteosarcoma patients into low- or high-risk groups that showed good and poor prognosis, respectively. High expression of these 2 key genes is associated with low-risk group in patients with osteosarcoma. We constructed an accurate nomogram to help clinicians assess the survival time of patients with osteosarcoma. The results of immune cell infiltration level showed that the high-risk group had lower levels of immune cell infiltration. GSEA revealed changes in immune regulation and hypoxia stress pathways in osteosarcoma under different metastatic states. Our study identified an excellent gene signature that could be helpful in improving the prognosis of patients with osteosarcoma.

1676-P: A Novel GLP1R/GCGR Dual Agonist, DA-1726 Elicits Weight Loss Superior to Semaglutide in Diet-Induced Obese Rats

DA-1726 showed superior weight loss efficacy compared to Semaglutide (SEMA) in DIO mice. In this following study, a dose-response of DA-1726 was evaluated to determine the maximum efficacy in DIO rats that are known to have good translatability to human studies. The half-life of DA-1726 was longer in rats compared to mice, however the in vitro activity of DA-1726 against rat glucagon receptor was less potent than in the mouse, and plasma protein binding was approximately 10-fold higher in rats. Therefore, we set the effective dose for rats higher than for mice. DA-1726 was injected twice a week for about 4 weeks, a dose-dependent and significant weight loss effect was observed. The minimum effective dose in DIO rats was found to be 250 nmol/kg. Then, the maximum body weight loss effects of DA-1726 (500 and 1000 nmole/kg) and SEMA were compared in the DIO rat. In this study, DA-1726 showed an excellent weight loss effect than SEMA (32.6% for DA-1726 at a high dose vs. 24.0% for SEMA, p&amp;lt;0.05). The low-dose DA-1726 group showed a similar effect compared to SEMA even though they consumed more food. The high-dose DA-1726 group showed similar food intake as SEMA, but led to a higher weight loss effect. After repeated dosing, the high-dose DA-1726 significantly increased the expression of energy metabolism-related genes (Ucp1, Ppargc1a) in white adipose tissues (WAT), supporting increased energy expenditure. So then, in order to confirm whether this increased gene change was a direct effect leading to weight loss or an indirect effect caused by weight loss, we assessed the changes in gene expression after single dosing. DA-1726 (250 and 500 nmol/kg) induced small but significant weight loss 3 days after a single injection (-7.6% and -9.5% vs. control). However, the genes expression (Ucp1, Ppargc1a) in WAT was significantly increased only in the 500 nmol/kg-treated group suggesting a direct effect. Therefore, DA-1726 is expected to elicit excellent weight loss effects in humans with a new mechanism. Disclosure Y.Chae: None. T.Kim: None. I.Jung: None. H.Kim: None. M.Kim: Employee; Dong-A ST.

PpMYB1 and PpNPR1 interact to enhance the resistance of peach fruit to Rhizopus stolonifer infection

MYB transcription factors play important role in stress-resistance of plants. Nevertheless, the function of MYB TFs in peach Rhizopus rot remains poorly understood. Herein, Pichia guilliermondii treatment activated resistance against Rhizopus stolonifer, as illustrated by reductions in the incidence rate and severity of Rhizopus rot disease, increased enzyme activities and gene expression of chitinase (CHI) and β-1,3-glucanase (GLU), and enhancement of energy production by inducing the activities and expression of H+-ATPase and Ca2+-ATPase, succinate dehydrogenase (SDH), and cytochrome c oxidase (CCO). Moreover, an R1-type MYB, PpMYB1, from peach fruit was induced during R. stolonifer infection and in response to P. guilliermondii treatment. PpMYB1 activated the transcription of PpCHI-EP3 and PpGLU-like genes and the energy metabolism-related gene PpH+-ATPase1 by directly targeting the MBS element. Importantly, PpMYB1 interacted with PpNPR1 to form a heterodimer, which was conducive to enhancing the activation of target gene transcription. Collectively, our findings suggest that PpMYB1 cooperates with PpNPR1 to positively regulate disease resistance by activating the disease defense system and energy metabolism in peaches.

Establishment of a risk score model for bladder urothelial carcinoma based on energy metabolism-related genes and their relationships with immune infiltration.

Bladder urothelial carcinoma (BLCA) is a common malignant tumor of the human urinary system, and a large proportion of BLCA patients have a poor prognosis. Therefore, there is an urgent need to find more efficient and sensitive biomarkers for the prognosis of BLCA patients in clinical practice. RNA sequencing (RNA-seq) data and clinical information were obtained from The Cancer Genome Atlas, and 584 energy metabolism-related genes (EMRGs) were obtained from the Reactome pathway database. Cox regression analysis and least absolute shrinkage and selection operator analysis were applied to assess prognostic genes and build a risk score model. The estimate and cibersort algorithms were used to explore the immune microenvironment, immune infiltration, and checkpoints in BLCA patients. Furthermore, we used the Human Protein Atlas database and our single-cell RNA-seq datasets of BLCA patients to verify the expression of 13 EMRGs at the protein and single-cell levels. We constructed a risk score model; the area under the curve of the model at 5 years was 0.792. The risk score was significantly correlated with the immune markers M0 macrophages, M2 macrophages, CD8 T cells, follicular helper T cells, regulatory T cells, and dendritic activating cells. Furthermore, eight immune checkpoint genes were significantly upregulated in the high-risk group. The risk score model can accurately predict the prognosis of BLCA patients and has clinical application value. In addition, according to the differences in immune infiltration and checkpoints, BLCA patients with the most significant benefit can be selected for immune checkpoint inhibitor therapy.

Uncovering the prominent role of satellite cells in paravertebral muscle development and aging by single-nucleus RNA sequencing

To uncover the role of satellite cells (SCs) in paravertebral muscle development and aging, we constructed a single-nucleus transcriptomic atlas of mouse paravertebral muscle across seven timepoints spanning the embryo (day 16.5) to old (month 24) stages. Eight cell types, including SCs, fast muscle cells, and slow muscle cells, were identified. An energy metabolism-related gene set, TCA CYCLE IN SENESCENCE, was enriched in SCs. Forty-two skeletal muscle disease-related genes were highly expressed in SCs and exhibited similar expression patterns. Among them, Pdha1 was the core gene in the TCA CYCLE IN SENESCENCE; Pgam2, Sod1, and Suclg1 are transcription factors closely associated with skeletal muscle energy metabolism. Transcription factor enrichment analysis of the 42 genes revealed that Myod1 and Mef2a were also highly expressed in SCs, which regulated Pdha1 expression and were associated with skeletal muscle development. These findings hint that energy metabolism may be pivotal in SCs development and aging. Three ligand-receptor pairs of extracellular matrix (ECM)-receptor interactions, Lamc1-Dag1, Lama2-Dag1, and Hspg2-Dag1, may play a vital role in SCs interactions with slow/fast muscle cells and SCs self-renewal. Finally, we built the first database of a skeletal muscle single-cell transcriptome, the Musculoskeletal Cell Atlas (http://www.mskca.tech), which lists 630,040 skeletal muscle cells and provides interactive visualization, a useful resource for revealing skeletal muscle cellular heterogeneity during development and aging. Our study could provide new targets and ideas for developing drugs to inhibit skeletal muscle aging and treat skeletal muscle diseases.

Identification of an energy metabolism-related six-gene signature for distinguishing and forecasting the prognosis of low-grade gliomas.

Low-grade gliomas (LGG) account for 20-25% of all gliomas. In this study, we assessed whether metabolic status was correlated with clinical outcomes in LGG patients using data from The Cancer Genome Atlas (TCGA). LGG patient data were collected from TCGA, and the Molecular Signature Database was used to extract gene sets related to energy metabolism. After performing a consensus-clustering algorithm, the LGG patients were divided into four clusters. We then compared the tumor prognosis, function, immune cell infiltration, checkpoint proteins, chemo-resistance, and cancer stem cells (CSC) between the two groups with the greatest prognostic difference. Using least absolute shrinkage and selection operator (LASSO) analysis, an energy metabolism-related signature was further developed. Energy metabolism-related signatures were applied to identify four clusters (C1, C2, C3, and C4) using a consensus-clustering algorithm. C1 LGG patients were more related to the synapse and had higher CSC scores, more chemo-resistance, and a better prognosis. C4 LGG was observed to have more immune-related pathways and better immunity. We then identified six energy metabolism-related genes (PYGL, HS3ST3B, NNMT, FMOD, CHST6, and B3GNT7) that can accurately predict LGG prognosis not only as a whole but also based on the independent predictions of each of these six genes. The energy metabolism-related subtypes of LGG were identified, which were strongly related to the immune microenvironment, immune checkpoint proteins, CSCs, chemo-resistance, prognosis, and LGG advancement. A signature of genes involved in energy metabolism could help to distinguish and predict the prognosis of LGG patients, and a promising method to discover patients that may benefit from LGG therapy.

Cellular senescence affects energy metabolism, immune infiltration and immunotherapeutic response in hepatocellular carcinoma

Aging is an inevitable consequence of life, characterized by a progressive decline in tissue and organ function and an increased risk of death. There is growing evidence that aging is closely related to tumor development and immune regulation. However, in hepatocellular carcinoma, the relationship between cellular senescence and immune infiltration, energy metabolism, chemokines, and immunotherapeutic response is unclear and needs further study. We first analyzed 274 cellular senescence-associated genes by the NMF algorithm and identified two cellular senescence-associated clusters. Subsequently, we compared the differences between the two clusters, in terms of immune infiltration, energy metabolism, chemokines, and immunotherapeutic response to treatment. We further constructed risk models using cellular senescence-associated signature genes that could effectively identify the two subpopulations. Finally, we validated the validity and robustness of the risk model using an external dataset. We found significant differences in survival prognosis between two cellular senescence-associated clusters. In addition, we found significant differences in immune cell infiltration, expression of energy metabolism-related genes, expression of chemokine-related genes, expression of immune checkpoint-related genes, Tumor Immune Dysfunction and Exclusion between the two clusters. Also, a scoring system associated with cellular senescence was developed and validated as an independent prognostic indicator. It was validated as an independent prognostic factor and immunotherapeutic predictor for HCC. It was validated as an independent prognostic factor and immunotherapeutic predictor for HCC. The cellular senescence-related scoring system was validated as an independent prognostic factor and immunotherapy predictor for HCC, and patients with low CSS were characterized by prolonged survival time. Our study confirmed the relationship between cellular senescence and immune cell infiltration, energy metabolism, chemokines, expression of immune checkpoint-related genes, and response to immunotherapy. This enhances our understanding of cellular senescence and tumor immune microenvironment, energy metabolism, chemokines, and provides new insights to improve immunotherapy outcomes in HCC patients. It provides new insights to improve the outcome of immunotherapy in HCC patients.

Host defense peptide-mimicking β-peptide polymer displaying strong antibacterial activity against cariogenic Streptococcus mutans

• A host defense peptide-mimicking β -peptide polymer tbubz-(DM 50 :CH 50 ) 30 shows superior antibacterial efficiency than metronidazole against S. mutans biofilm. • The β -peptide polymer inhibits the formation of S. mutans biofilm. • The β -peptide polymer disperses mature S. mutans biofilm. • The β -peptide polymer exhibits high antibacterial activity and low cell toxicity. Cariogenic Streptococcus mutans ( S. mutans ) is a leading cause of bacterial-induced oral diseases. Current strategies to kill bacteria based on Host defense peptide (HDP) mimicking polymers hold promise to treat oral bacterial infection. Here, we explore the impact of hydrophobic subunit and chain length variation on the antibacterial and antibiofilm activity of β -peptide polymers. The physicochemical and biological properties, such as the toxicity, the antibacterial activity, and the effect on bacterial transcription of β -peptide polymers, were systematically investigated with numerous techniques. The results exhibited that the optimal β -peptide polymer has low toxicity towards human periodontal ligament fibroblasts, and β -peptide polymers (especially P3) have more excellent antibacterial activity against S. mutans than metronidazole. In addition, β -peptide polymers inhibited the reversible and irreversible bacterial adhesion during the formation of biofilms. The polymer can promote biofilm dispersion by decreasing the hydrophobicity of bacterial cells after adhering to cell surfaces. Analysis of the transcriptome for S. mutans treated with β -peptide polymers demonstrated that β -peptide polymers could reduce the cariogenicity of S. mutans by impacting the transcription of the energy and acid metabolism-related genes. β -peptide polymers are promising antimicrobial agents in clinical dentistry due to their high antibacterial efficiency and low toxicity.